Capacidades de la inteligencia artificial aplicadas a la gestión clínica hospitalaria: revisión sistemática
Artificial intelligence capabilities in hospital clinical management: a systematic reviewBarra lateral del artículo
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Introducción: La inteligencia artificial (IA) se incorpora progresivamente a la gestión hospitalaria al apoyar el análisis de datos clínicos, las decisiones diagnósticas y la organización de recursos. Objetivo: Identificar y analizar las capacidades de IA aplicadas a la gestión clínica y administrativa hospitalaria, así como los modelos y algoritmos empleados. Método: Se realizó una revisión sistemática conforme a PRISMA 2020. Se consultaron Scopus, PubMed, Web of Science, SciELO y Redalyc; se incluyeron artículos originales de acceso completo, publicados entre 2021 y 2025, realizados en hospitales y con aplicación explícita de IA. De 1.195 registros identificados, se seleccionaron 58 estudios. Resultados: Cincuenta estudios (86,2 %) abordaron la gestión clínica y nueve (15,5 %) la administrativa; un estudio contribuyó a ambas categorías. El procesamiento de información fue la capacidad predominante (54/58; 93,1 %), principalmente mediante análisis y modelado de datos, y análisis de imágenes. Predominaron los modelos de aprendizaje profundo, especialmente las redes neuronales convolucionales, y los algoritmos de aprendizaje automático para clasificación y predicción. En el ámbito administrativo, las aplicaciones se concentraron en la gestión de camas y citas, el control de procesos y la atención al usuario mediante procesamiento de lenguaje natural. Conclusión: La IA muestra una contribución relevante a la precisión diagnóstica, el pronóstico y la eficiencia asistencial. Su uso administrativo sigue siendo menos frecuente; por ello, son necesarios estudios de implementación que evalúen desempeño, seguridad, equidad y efecto organizacional.
Introduction: Artificial intelligence (AI) is increasingly incorporated into hospital management by supporting clinical data analysis, diagnostic decisions, and resource organization. Objective: To identify and analyze AI capabilities applied to hospital clinical and administrative management, as well as the models and algorithms used. Methods: A systematic review was conducted in accordance with PRISMA 2020. Scopus, PubMed, Web of Science, SciELO, and Redalyc were searched. Original full-text studies published between 2021 and 2025, conducted in hospitals, and explicitly applying AI were included. Of 1,195 records identified, 58 studies were selected. Results: Fifty studies (86.2%) addressed clinical management and nine (15.5%) administrative management; one study contributed to both categories. Information processing was the predominant capability (54/58; 93.1%), mainly through data analysis and modeling and image analysis. Deep-learning models, particularly convolutional neural networks, and machine-learning algorithms for classification and prediction predominated. Administrative applications focused on bed and appointment management, process control, and user support through natural language processing. Conclusion: AI contributes meaningfully to diagnostic accuracy, prognosis, and care efficiency. Its administrative use remains less frequent; implementation research assessing performance, safety, equity, and organizational impact is needed.
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